Automated Detection of Welding Defects without Segmentation

Abstract Substantial research has been performed on automated detection and classification of welding defects in continuous welds using X-ray imaging. Typically, the detection follows a pattern recognition schema (segmentation, feature extraction and classification). In computer vision community, however, many object detection and classification problems, like face and human detection, have been recently solved - without segmentation- using sliding-windows and novel features like local binary patterns extracted from saliency maps. For this reason, we propose in this paper the use of sliding-windows with the mentioned features to perform automatically the automated detection of welding defects. In the experiments, we analyzed 5000 detection windows (24x24 pixels) and 572 intensity features from 10 representative X-ray images. Cross validation yielded a detection performance of 94% using a support vector machine classifier with only 14 selected features. The method was implemented and tested on real X-ray images showing high effectiveness. We believe that the proposed approach opens new possibilities in the field of automated detection of welding defects